Improved WLP and GWP lower bounds based on exact integer programming

By using exact integer programming (IP) (integer programming in infinite precision) bounds on the word-length patterns (WLPs) and generalized word-length patterns (GWPs) for fractional factorial designs are improved. In the literature, bounds on WLPs are formulated as linear programming (LP) problems. Although the solutions to such problems must be integral, the optimization is performed without the integrality constraints. Two examples of this approach are bounds on the number of words of length four for resolution IV regular designs, and a lower bound for the GWP of two-level orthogonal arrays. We reformulate these optimization problems as IP problems with additional valid constraints in the literature and improve the bounds in many cases. We compare the improved bound to the enumeration results in the literature to find many cases for which our bounds are achieved. By using the constraints in our integer programs we prove that f(16λ,2,4)?9$f(16\lambda ,2,4)?9$ if λ$\lambda$ is odd where f(2^tλ,2,t)$f(2^t\lambda ,2,t)$ is the maximum n   for which an OA(N,n,2,t)$\mathrm{OA}(N,n,2,t)$ exists. We also present a theorem for constructing GMA OA(N,N/2-u,2,3)$\mathrm{OA}(N,N/2-u,2,3)$ for u=1,…,5$u=1,\dots ,5$.     

Doing something about the weather

Recent developments in weather forecasting have created the potential for the operations research and management science (OR/MS) community to have a tremendous impact in distilling weather information into valuable decision tools. Weather-sensitive applications include transport, electric utilities, agriculture, and public emergency management. This article surveys existing research and practice using OR/MS tools to integrate weather forecasts in decision-making. Because the conditions that created the potential for OR/MS contributions—in particular an explosion in the amount of relevant forecast data—are quite recent, the amount of existing OR/MS work is modest. This article also describes promising but unexplored research opportunities for the OR/MS community.     

可变维核心矩阵LU分解方法

在线性规划问题的发展过程中,基的分解技术一直是求解线性规划问题算法实现的一个重要问题。在传统的线性规划算法中,基逆的乘积形式(PFI)方法和LU分解方法很好的解决了基逆的稀疏性、累计误差等问题。随着线性规划动态分解和核心矩阵的出现,矩阵的动态分解成为了一个新的研究课题。本文主要研究和分析单纯形算法中的核心矩阵的动态分解和存储方法,将经典的LU分解方法应用于核心矩阵的动态分解和存储中,保持了核心距阵的数值稳定性和稀疏性。同时,本文提出置换消元方法可以大大减少LU更新的时间。     

Optimization models for the tool change scheduling problem

Traditional machine scheduling literature generally assumes that a machine is available at all times. Yet this assumption may not be accurate in real manufacturing systems. In many cases, a machine's tool must be changed after it has continuously worked for a period of time. This paper deals with a single machine scheduling problem subject to tool wear, given the allowed maximum continuous working time of the machine is _TL$T_{\mathrm{L}}$ (tool life) and the tool change time is _TC$T_{\mathrm{C}}$. Job processing and tool changes are scheduled simultaneously. In this paper, we examine this problem to minimize the total tardiness of jobs. Two mixed binary integer programming models are developed to optimally solve this problem. Computational experiments are performed to evaluate the models’ efficiency.     

Simplest optimal guillotine cutting patterns for strips of identical circles

The manufacturing industry often uses the cutting and stamping process to divide stock plates into circles. A guillotine machine cuts the plate into strips for stamping at the cutting stage, and then a stamping press punches out the circles from the strips at the stamping stage. The problem discussed is to cut a plate into strips of identical circles such that the number of circles is maximized. A dynamic programming algorithm is presented for generating the simplest optimal cutting patterns of the strips. The computational results indicate that the algorithm is much efficient in simplifying the cutting process.     

Compromise allocation in multivariate stratified surveys with stochastic quadratic cost function

In many real life situations the linear cost function does not approximate the actual cost incurred adequately. The cost of traveling between the units selected in the sample within a stratum is significant, instead of linear cost function. In this paper, we have considered the problem of finding a compromise allocation for a multivariate stratified sample survey with a significant travel cost within strata is formulated as a problem of non-linear stochastic programming with multiple objective functions. The compromise solutions are obtained through Chebyshev approximation technique, D ₁- distance and goal programming. A numerical example is presented to illustrate the computational details of the proposed methods.     

A Stochastic Approach to General Nonlinear Programming Problems

Nonlinear programming problem is the general case of mathematical programming problem such that both the objective and constraint functions are nonlinear and is the most difficult case of smooth optimization problem to solve. In this article, we suggest a stochastic search method to general nonlinear programming problems which is not an iterative algorithm but it is an interior point method. The proposed method finds the near-optimal solution to the problem. The results of a few numerical studies are reported. The efficiency of the new method is compared and is found to be reasonable.     

基于双层规划的交通疏散中车辆出发与交通控制综合优化

在有组织的区域性疏散中,从需求调节(即疏散车辆出发安排)和供给管理(即交通管控)两方面对疏散交通流进行合理组织,是提高疏散效率的有效途径。论文立足于疏散车辆出发组织与路网交通管控之间的双层决策关系,建立双层规划模型对集结点疏散车辆的发车频率、路线和交叉口控制参数进行综合优化,其中上层模型通过优化信号交叉口的相位绿灯时间即绿信比以降低平均延误,其决策影响到交叉口通行能力等供给特性;下层模型通过优化疏散车辆的分批出发时间与路线以压缩疏散总时间,其决策影响到交叉口流量等需求特性。设计了基于遗产算法的求解步骤,给出了一个数值算例。将模型优化方案和只从交叉口控制参数出发的单方面优化模式所得结果进行了比较,结果表明只从调整绿灯时间着手不结合车辆的出发组织,很难有效降低延误和压缩疏散时间。     

Policies for knowledge refreshing in databases

Knowledge discovery in databases (KDD) provides organizations necessary tools to sift through vast data stores to extract knowledge. This process supports and improves decision making in organizations. In this paper, we introduce and define the concept of knowledge refreshing, a critical step to ensure the quality and timeliness of knowledge discovered in a KDD process. This has been unfortunately overlooked by prior researchers. Specifically, we study knowledge refreshing from the perspective of when to refresh knowledge so that the total system cost over a time horizon is minimized. We propose a model for knowledge refreshing, and a dynamic programming methodology for developing optimal strategies. We demonstrate the effectiveness of the proposed methodology using data from a real world application. The proposed methodology provides decision makers guidance in running KDD effectively and efficiently.     

研究生创新能力培养探析

通过分析研究生创新能力培养的基本特点,结合中国研究生教 育培养的传统教育模式与时代发展要求不相适应的现状,对研究生创新能力的培养途径进行 了初步探讨。